Overview

Dataset statistics

Number of variables22
Number of observations13167
Missing cells66800
Missing cells (%)23.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory176.0 B

Variable types

Categorical10
DateTime2
Numeric10

Alerts

UniqueFireIdentifier has a high cardinality: 13139 distinct values High cardinality
IncidentName has a high cardinality: 11591 distinct values High cardinality
POOCounty has a high cardinality: 1068 distinct values High cardinality
key has a high cardinality: 11146 distinct values High cardinality
DailyAcres is highly correlated with Final Fire Acre QuantityHigh correlation
Final Fire Acre Quantity is highly correlated with DailyAcresHigh correlation
DailyAcres is highly correlated with EstimatedCostToDateHigh correlation
EstimatedCostToDate is highly correlated with DailyAcresHigh correlation
DailyAcres is highly correlated with Final Fire Acre QuantityHigh correlation
Final Fire Acre Quantity is highly correlated with DailyAcresHigh correlation
FireMgmtComplexity is highly correlated with IncidentTypeCategoryHigh correlation
IncidentTypeCategory is highly correlated with FireMgmtComplexity and 1 other fieldsHigh correlation
PredominantFuelGroup is highly correlated with IncidentTypeCategoryHigh correlation
FireCause is highly correlated with POOState and 2 other fieldsHigh correlation
DailyAcres is highly correlated with EstimatedCostToDateHigh correlation
EstimatedCostToDate is highly correlated with DailyAcresHigh correlation
FireMgmtComplexity is highly correlated with POOState and 1 other fieldsHigh correlation
POOState is highly correlated with FireCause and 4 other fieldsHigh correlation
POOLandownerKind is highly correlated with POOState and 1 other fieldsHigh correlation
IncidentTypeCategory is highly correlated with FireMgmtComplexityHigh correlation
PredominantFuelGroup is highly correlated with POOStateHigh correlation
FireDiscoveryYear is highly correlated with FireCause and 1 other fieldsHigh correlation
FireDiscoveryMonth is highly correlated with FireCause and 1 other fieldsHigh correlation
total_str_damaged is highly correlated with total_str_threatenedHigh correlation
total_str_threatened is highly correlated with total_str_damagedHigh correlation
FireCause has 800 (6.1%) missing values Missing
FireMgmtComplexity has 2028 (15.4%) missing values Missing
FireToDate has 1988 (15.1%) missing values Missing
POOLandownerKind has 5630 (42.8%) missing values Missing
PredominantFuelGroup has 11678 (88.7%) missing values Missing
Number Injuries has 8530 (64.8%) missing values Missing
Number Fatalities has 8530 (64.8%) missing values Missing
Final Fire Acre Quantity has 8530 (64.8%) missing values Missing
total_str_damaged has 8530 (64.8%) missing values Missing
total_str_threatened has 8530 (64.8%) missing values Missing
TotalDays has 1988 (15.1%) missing values Missing
EstimatedCostToDate is highly skewed (γ1 = 26.88381527) Skewed
Number Fatalities is highly skewed (γ1 = 66.68326854) Skewed
total_str_damaged is highly skewed (γ1 = 46.40339685) Skewed
total_str_threatened is highly skewed (γ1 = 47.04408982) Skewed
UniqueFireIdentifier is uniformly distributed Uniform
IncidentName is uniformly distributed Uniform
key is uniformly distributed Uniform
EstimatedCostToDate has 1590 (12.1%) zeros Zeros
Number Injuries has 4447 (33.8%) zeros Zeros
Number Fatalities has 4610 (35.0%) zeros Zeros
total_str_damaged has 3777 (28.7%) zeros Zeros
total_str_threatened has 3019 (22.9%) zeros Zeros
TotalDays has 2439 (18.5%) zeros Zeros

Reproduction

Analysis started2022-06-04 14:26:45.648189
Analysis finished2022-06-04 14:27:29.149274
Duration43.5 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

UniqueFireIdentifier
Categorical

HIGH CARDINALITY
UNIFORM

Distinct13139
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size103.0 KiB
2017-MSMSS-004192
 
3
2014-ORPRD-000649
 
2
2014-NVHTF-030321
 
2
2017-FLFLS-2017140228
 
2
2017-KYKYS-17-4001
 
2
Other values (13134)
13156 

Length

Max length22
Median length17
Mean length17.3403205
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13112 ?
Unique (%)99.6%

Sample

1st row2020-TXTXS-200317
2nd row2016-FLFLS-2016120122
3rd row2014-LASBR-000015
4th row2018-TXTXS-000298
5th row2016-CAKRN-032270

Common Values

ValueCountFrequency (%)
2017-MSMSS-0041923
 
< 0.1%
2014-ORPRD-0006492
 
< 0.1%
2014-NVHTF-0303212
 
< 0.1%
2017-FLFLS-20171402282
 
< 0.1%
2017-KYKYS-17-40012
 
< 0.1%
2014-ORDEF-0003292
 
< 0.1%
2014-WAOWF-0003562
 
< 0.1%
2017-TNTNS-1000052
 
< 0.1%
2015-CASNF-0016892
 
< 0.1%
2014-UTSLD-0005312
 
< 0.1%
Other values (13129)13146
99.8%

Length

2022-06-04T10:27:29.352485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-msmss-0041923
 
< 0.1%
2015-mspri-scd3
 
< 0.1%
2017-okoks-1700262
 
< 0.1%
2014-waolf-0000732
 
< 0.1%
2014-orwif-1402742
 
< 0.1%
2014-wybtf-0000162
 
< 0.1%
2015-flfls-20150200322
 
< 0.1%
2018-okecu-1801432
 
< 0.1%
2015-okosa-0150392
 
< 0.1%
2014-azknf-0005822
 
< 0.1%
Other values (13134)13152
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IncidentName
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11591
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size103.0 KiB
Willow
 
13
Bear Creek
 
13
Cottonwood
 
10
Rock Creek
 
9
WALKER
 
9
Other values (11586)
13113 

Length

Max length50
Median length10
Mean length10.33196628
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10592 ?
Unique (%)80.4%

Sample

1st rowPark
2nd rowQuincy
3rd rowMALLARD FIRE
4th rowArrington Ranch
5th rowRANGE

Common Values

ValueCountFrequency (%)
Willow13
 
0.1%
Bear Creek13
 
0.1%
Cottonwood10
 
0.1%
Rock Creek9
 
0.1%
WALKER9
 
0.1%
Powerline9
 
0.1%
North Fork9
 
0.1%
Rattlesnake8
 
0.1%
Dry Creek8
 
0.1%
Willow Creek8
 
0.1%
Other values (11581)13071
99.3%

Length

2022-06-04T10:27:29.654094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
creek1255
 
5.2%
fire857
 
3.5%
road617
 
2.5%
rd382
 
1.6%
2295
 
1.2%
river276
 
1.1%
lake236
 
1.0%
mountain235
 
1.0%
complex210
 
0.9%
ridge165
 
0.7%
Other values (7254)19741
81.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FireDiscoveryDateTime
Date

Distinct12788
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size103.0 KiB
Minimum2014-04-08 21:00:00+00:00
Maximum2022-03-07 20:15:00+00:00
2022-06-04T10:27:29.935799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:30.258255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FireCause
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing800
Missing (%)6.1%
Memory size103.0 KiB
Human
5627 
Natural
4571 
Undetermined
1277 
Unknown
892 

Length

Max length12
Median length7
Mean length6.606290936
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHuman
2nd rowHuman
3rd rowNatural
4th rowHuman
5th rowUndetermined

Common Values

ValueCountFrequency (%)
Human5627
42.7%
Natural4571
34.7%
Undetermined1277
 
9.7%
Unknown892
 
6.8%
(Missing)800
 
6.1%

Length

2022-06-04T10:27:30.581152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T10:27:30.763750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
human5627
45.5%
natural4571
37.0%
undetermined1277
 
10.3%
unknown892
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DailyAcres
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4797
Distinct (%)36.5%
Missing32
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4818.357033
Minimum0
Maximum1032648
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:31.032230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q173.6
median339
Q31313
95-th percentile18098.69
Maximum1032648
Range1032648
Interquartile range (IQR)1239.4

Descriptive statistics

Standard deviation26533.5314
Coefficient of variation (CV)5.506759091
Kurtosis463.2699652
Mean4818.357033
Median Absolute Deviation (MAD)334
Skewness17.41998985
Sum63289119.63
Variance704028288.4
MonotonicityNot monotonic
2022-06-04T10:27:31.335224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1479
 
3.6%
1402
 
3.1%
2221
 
1.7%
0.5135
 
1.0%
5125
 
0.9%
3125
 
0.9%
300117
 
0.9%
100113
 
0.9%
150112
 
0.9%
20093
 
0.7%
Other values (4787)11213
85.2%
ValueCountFrequency (%)
03
 
< 0.1%
0.031
 
< 0.1%
0.1479
3.6%
0.151
 
< 0.1%
0.257
 
0.4%
0.211
 
< 0.1%
0.221
 
< 0.1%
0.231
 
< 0.1%
0.2555
 
0.4%
0.381
 
0.6%
ValueCountFrequency (%)
10326481
< 0.1%
9633091
< 0.1%
7792921
< 0.1%
6627001
< 0.1%
5052731
< 0.1%
4980431
< 0.1%
4591231
< 0.1%
4216131
< 0.1%
4168211
< 0.1%
4137171
< 0.1%

EstimatedCostToDate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3568
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1622129.277
Minimum0
Maximum637428216
Zeros1590
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:31.649380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median12600
Q3200000
95-th percentile5352170
Maximum637428216
Range637428216
Interquartile range (IQR)199000

Descriptive statistics

Standard deviation12448184.17
Coefficient of variation (CV)7.673977867
Kurtosis1057.087954
Mean1622129.277
Median Absolute Deviation (MAD)12600
Skewness26.88381527
Sum2.135857619 × 1010
Variance1.549572891 × 1014
MonotonicityNot monotonic
2022-06-04T10:27:31.941983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01590
 
12.1%
1000494
 
3.8%
5000369
 
2.8%
10000354
 
2.7%
1319
 
2.4%
2000250
 
1.9%
50000225
 
1.7%
15000207
 
1.6%
100000202
 
1.5%
20000199
 
1.5%
Other values (3558)8958
68.0%
ValueCountFrequency (%)
01590
12.1%
0.012
 
< 0.1%
1319
 
2.4%
1.671
 
< 0.1%
1.826
 
< 0.1%
226
 
0.2%
2.51
 
< 0.1%
32
 
< 0.1%
105
 
< 0.1%
12.41
 
< 0.1%
ValueCountFrequency (%)
6374282161
< 0.1%
5425399521
< 0.1%
4944014201
< 0.1%
2711475121
< 0.1%
2625000001
< 0.1%
2300000001
< 0.1%
2010000002
< 0.1%
2000000001
< 0.1%
1930000001
< 0.1%
1915262841
< 0.1%

FireMgmtComplexity
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing2028
Missing (%)15.4%
Memory size103.0 KiB
Type 4 Incident
5231 
Type 5 Incident
3725 
Type 3 Incident
1782 
Type 2 Incident
 
246
Type 1 Incident
 
154

Length

Max length22
Median length15
Mean length15.00062842
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowType 5 Incident
2nd rowType 5 Incident
3rd rowType 5 Incident
4th rowType 3 Incident
5th rowType 4 Incident

Common Values

ValueCountFrequency (%)
Type 4 Incident5231
39.7%
Type 5 Incident3725
28.3%
Type 3 Incident1782
 
13.5%
Type 2 Incident246
 
1.9%
Type 1 Incident154
 
1.2%
Type 2 Prescribed Fire1
 
< 0.1%
(Missing)2028
 
15.4%

Length

2022-06-04T10:27:32.274636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T10:27:32.464709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
type11139
33.3%
incident11138
33.3%
45231
15.7%
53725
 
11.1%
31782
 
5.3%
2247
 
0.7%
1154
 
0.5%
prescribed1
 
< 0.1%
fire1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FireToDate
Date

MISSING

Distinct8997
Distinct (%)80.5%
Missing1988
Missing (%)15.1%
Memory size103.0 KiB
Minimum2014-10-20 17:15:00+00:00
Maximum2022-03-07 20:15:00+00:00
2022-06-04T10:27:32.771691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:33.073263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

POOCounty
Categorical

HIGH CARDINALITY

Distinct1068
Distinct (%)8.1%
Missing3
Missing (%)< 0.1%
Memory size103.0 KiB
Yukon-Koyukuk
 
627
Idaho
 
274
Osage
 
221
Fairbanks North Star
 
147
Lincoln
 
137
Other values (1063)
11758 

Length

Max length28
Median length7
Mean length7.527119417
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)1.7%

Sample

1st rowShackelford
2nd rowBrevard
3rd rowCameron
4th rowCarson
5th rowKern

Common Values

ValueCountFrequency (%)
Yukon-Koyukuk627
 
4.8%
Idaho274
 
2.1%
Osage221
 
1.7%
Fairbanks North Star147
 
1.1%
Lincoln137
 
1.0%
Southeast Fairbanks133
 
1.0%
Latimer127
 
1.0%
Bethel124
 
0.9%
Polk123
 
0.9%
Adair121
 
0.9%
Other values (1058)11130
84.5%

Length

2022-06-04T10:27:33.386416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
yukon-koyukuk627
 
4.3%
fairbanks280
 
1.9%
idaho274
 
1.9%
osage221
 
1.5%
north160
 
1.1%
star147
 
1.0%
san140
 
0.9%
lincoln137
 
0.9%
southeast133
 
0.9%
latimer127
 
0.9%
Other values (1091)12491
84.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

POOState
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size103.0 KiB
US-OK
1648 
US-AK
1359 
US-FL
976 
US-CA
926 
US-ID
905 
Other values (44)
7353 

Length

Max length6
Median length5
Mean length5.000075947
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowUS-TX
2nd rowUS-FL
3rd rowUS-LA
4th rowUS-TX
5th rowUS-CA

Common Values

ValueCountFrequency (%)
US-OK1648
 
12.5%
US-AK1359
 
10.3%
US-FL976
 
7.4%
US-CA926
 
7.0%
US-ID905
 
6.9%
US-MT716
 
5.4%
US-TX613
 
4.7%
US-AZ544
 
4.1%
US-MS529
 
4.0%
US-WA519
 
3.9%
Other values (39)4432
33.7%

Length

2022-06-04T10:27:33.660446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us-ok1648
 
12.5%
us-ak1359
 
10.3%
us-fl976
 
7.4%
us-ca926
 
7.0%
us-id905
 
6.9%
us-mt716
 
5.4%
us-tx613
 
4.7%
us-az544
 
4.1%
us-ms529
 
4.0%
us-wa519
 
3.9%
Other values (39)4432
33.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

POOLandownerKind
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing5630
Missing (%)42.8%
Memory size103.0 KiB
Federal
5057 
Private
1681 
Other
793 
#
 
6

Length

Max length7
Median length7
Mean length6.784795011
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate
2nd rowFederal
3rd rowPrivate
4th rowFederal
5th rowFederal

Common Values

ValueCountFrequency (%)
Federal5057
38.4%
Private1681
 
12.8%
Other793
 
6.0%
#6
 
< 0.1%
(Missing)5630
42.8%

Length

2022-06-04T10:27:33.936293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T10:27:34.136972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
federal5057
67.1%
private1681
 
22.3%
other793
 
10.5%
6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IncidentTypeCategory
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.0 KiB
WF
13048 
CX
 
90
RX
 
29

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWF
2nd rowWF
3rd rowWF
4th rowWF
5th rowWF

Common Values

ValueCountFrequency (%)
WF13048
99.1%
CX90
 
0.7%
RX29
 
0.2%

Length

2022-06-04T10:27:34.384624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T10:27:34.552251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
wf13048
99.1%
cx90
 
0.7%
rx29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PredominantFuelGroup
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing11678
Missing (%)88.7%
Memory size103.0 KiB
Timber
676 
Grass-Shrub
366 
Grass
278 
Brush
160 
Slash
 
9

Length

Max length11
Median length6
Mean length6.928811283
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrush
2nd rowTimber
3rd rowTimber
4th rowGrass-Shrub
5th rowGrass-Shrub

Common Values

ValueCountFrequency (%)
Timber676
 
5.1%
Grass-Shrub366
 
2.8%
Grass278
 
2.1%
Brush160
 
1.2%
Slash9
 
0.1%
(Missing)11678
88.7%

Length

2022-06-04T10:27:34.773184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-04T10:27:34.957694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
timber676
45.4%
grass-shrub366
24.6%
grass278
18.7%
brush160
 
10.7%
slash9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FireDiscoveryYear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.510747
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:35.228136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2017
Q32019
95-th percentile2021
Maximum2022
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.19887404
Coefficient of variation (CV)0.001089894586
Kurtosis-1.026714932
Mean2017.510747
Median Absolute Deviation (MAD)2
Skewness0.2790396247
Sum26564564
Variance4.835047042
MonotonicityNot monotonic
2022-06-04T10:27:35.466281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20162180
16.6%
20152138
16.2%
20172117
16.1%
20181616
12.3%
20201425
10.8%
20211400
10.6%
20191215
9.2%
2014765
 
5.8%
2022311
 
2.4%
ValueCountFrequency (%)
2014765
 
5.8%
20152138
16.2%
20162180
16.6%
20172117
16.1%
20181616
12.3%
20191215
9.2%
20201425
10.8%
20211400
10.6%
2022311
 
2.4%
ValueCountFrequency (%)
2022311
 
2.4%
20211400
10.6%
20201425
10.8%
20191215
9.2%
20181616
12.3%
20172117
16.1%
20162180
16.6%
20152138
16.2%
2014765
 
5.8%

FireDiscoveryMonth
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.205437837
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:35.727978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.712617441
Coefficient of variation (CV)0.4371355434
Kurtosis-0.6788764469
Mean6.205437837
Median Absolute Deviation (MAD)2
Skewness-0.05346843016
Sum81707
Variance7.358293381
MonotonicityNot monotonic
2022-06-04T10:27:35.944831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
72495
18.9%
82071
15.7%
61736
13.2%
31483
11.3%
4985
 
7.5%
2948
 
7.2%
9791
 
6.0%
5733
 
5.6%
11668
 
5.1%
10520
 
3.9%
Other values (2)737
 
5.6%
ValueCountFrequency (%)
1473
 
3.6%
2948
 
7.2%
31483
11.3%
4985
 
7.5%
5733
 
5.6%
61736
13.2%
72495
18.9%
82071
15.7%
9791
 
6.0%
10520
 
3.9%
ValueCountFrequency (%)
12264
 
2.0%
11668
 
5.1%
10520
 
3.9%
9791
 
6.0%
82071
15.7%
72495
18.9%
61736
13.2%
5733
 
5.6%
4985
 
7.5%
31483
11.3%

key
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11146
Distinct (%)84.7%
Missing3
Missing (%)< 0.1%
Memory size103.0 KiB
2015-06-22AKYUKON-KOYUKUK
 
38
2016-07-15AKYUKON-KOYUKUK
 
30
2015-06-23AKYUKON-KOYUKUK
 
29
2015-06-20AKYUKON-KOYUKUK
 
21
2015-06-21AKYUKON-KOYUKUK
 
20
Other values (11141)
13026 

Length

Max length40
Median length19
Mean length19.52711942
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10043 ?
Unique (%)76.3%

Sample

1st row2020-07-21TXSHACKELFORD
2nd row2016-03-09FLBREVARD
3rd row2014-08-18LACAMERON
4th row2018-03-23TXCARSON
5th row2016-08-26CAKERN

Common Values

ValueCountFrequency (%)
2015-06-22AKYUKON-KOYUKUK38
 
0.3%
2016-07-15AKYUKON-KOYUKUK30
 
0.2%
2015-06-23AKYUKON-KOYUKUK29
 
0.2%
2015-06-20AKYUKON-KOYUKUK21
 
0.2%
2015-06-21AKYUKON-KOYUKUK20
 
0.2%
2015-08-11IDIDAHO19
 
0.1%
2016-07-14AKBETHEL18
 
0.1%
2015-07-31CATRINITY17
 
0.1%
2015-06-24AKYUKON-KOYUKUK17
 
0.1%
2016-06-26AKYUKON-KOYUKUK17
 
0.1%
Other values (11136)12938
98.3%

Length

2022-06-04T10:27:36.232091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
north147
 
1.0%
star147
 
1.0%
fairbanks133
 
0.9%
river82
 
0.6%
arctic74
 
0.5%
flore74
 
0.5%
elder53
 
0.4%
horn52
 
0.4%
angeles51
 
0.3%
2015-06-22akyukon-koyukuk38
 
0.3%
Other values (11221)13886
94.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number Injuries
Real number (ℝ≥0)

MISSING
ZEROS

Distinct13
Distinct (%)0.3%
Missing8530
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean0.1414707785
Minimum0
Maximum33
Zeros4447
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:36.488408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.465831368
Coefficient of variation (CV)10.36137203
Kurtosis347.319043
Mean0.1414707785
Median Absolute Deviation (MAD)0
Skewness17.79779528
Sum656
Variance2.148661598
MonotonicityNot monotonic
2022-06-04T10:27:36.688065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
04447
33.8%
1116
 
0.9%
223
 
0.2%
317
 
0.1%
68
 
0.1%
307
 
0.1%
55
 
< 0.1%
45
 
< 0.1%
74
 
< 0.1%
172
 
< 0.1%
Other values (3)3
 
< 0.1%
(Missing)8530
64.8%
ValueCountFrequency (%)
04447
33.8%
1116
 
0.9%
223
 
0.2%
317
 
0.1%
45
 
< 0.1%
55
 
< 0.1%
68
 
0.1%
74
 
< 0.1%
172
 
< 0.1%
191
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
307
0.1%
261
 
< 0.1%
191
 
< 0.1%
172
 
< 0.1%
74
 
< 0.1%
68
0.1%
55
 
< 0.1%
45
 
< 0.1%
317
0.1%

Number Fatalities
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct6
Distinct (%)0.1%
Missing8530
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean0.027172741
Minimum0
Maximum85
Zeros4610
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:36.915508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum85
Range85
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.257126041
Coefficient of variation (CV)46.26423375
Kurtosis4505.552823
Mean0.027172741
Median Absolute Deviation (MAD)0
Skewness66.68326854
Sum126
Variance1.580365883
MonotonicityNot monotonic
2022-06-04T10:27:37.138696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
04610
35.0%
121
 
0.2%
52
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
851
 
< 0.1%
(Missing)8530
64.8%
ValueCountFrequency (%)
04610
35.0%
121
 
0.2%
32
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
851
 
< 0.1%
ValueCountFrequency (%)
851
 
< 0.1%
52
 
< 0.1%
41
 
< 0.1%
32
 
< 0.1%
121
 
0.2%
04610
35.0%

Final Fire Acre Quantity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2114
Distinct (%)45.6%
Missing8530
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean3448.160133
Minimum0.001
Maximum542246.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:37.427148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.1
Q14
median175
Q3750
95-th percentile9637.3
Maximum542246.3
Range542246.299
Interquartile range (IQR)746

Descriptive statistics

Standard deviation23629.93714
Coefficient of variation (CV)6.852911765
Kurtosis317.7856194
Mean3448.160133
Median Absolute Deviation (MAD)174.58
Skewness16.17049936
Sum15989118.53
Variance558373929.3
MonotonicityNot monotonic
2022-06-04T10:27:37.779489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1192
 
1.5%
1164
 
1.2%
298
 
0.7%
0.0163
 
0.5%
0.259
 
0.4%
359
 
0.4%
0.553
 
0.4%
542
 
0.3%
439
 
0.3%
0.2537
 
0.3%
Other values (2104)3831
29.1%
(Missing)8530
64.8%
ValueCountFrequency (%)
0.0018
 
0.1%
0.0023
 
< 0.1%
0.0071
 
< 0.1%
0.0163
0.5%
0.0214
 
0.1%
0.037
 
0.1%
0.047
 
0.1%
0.059
 
0.1%
0.063
 
< 0.1%
0.071151
 
< 0.1%
ValueCountFrequency (%)
542246.34
< 0.1%
4591232
< 0.1%
3181561
 
< 0.1%
305655.12
< 0.1%
2049471
 
< 0.1%
1935661
 
< 0.1%
1930581
 
< 0.1%
1911251
 
< 0.1%
1720004
< 0.1%
160074.3471
 
< 0.1%

total_str_damaged
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct50
Distinct (%)1.1%
Missing8530
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean11.59564374
Minimum0
Maximum19998
Zeros3777
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:38.232453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum19998
Range19998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation420.8244888
Coefficient of variation (CV)36.29160213
Kurtosis2195.931488
Mean11.59564374
Median Absolute Deviation (MAD)0
Skewness46.40339685
Sum53769
Variance177093.2504
MonotonicityNot monotonic
2022-06-04T10:27:38.557906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03777
28.7%
1481
 
3.7%
2157
 
1.2%
363
 
0.5%
429
 
0.2%
612
 
0.1%
512
 
0.1%
78
 
0.1%
88
 
0.1%
127
 
0.1%
Other values (40)83
 
0.6%
(Missing)8530
64.8%
ValueCountFrequency (%)
03777
28.7%
1481
 
3.7%
2157
 
1.2%
363
 
0.5%
429
 
0.2%
512
 
0.1%
612
 
0.1%
78
 
0.1%
88
 
0.1%
95
 
< 0.1%
ValueCountFrequency (%)
199982
< 0.1%
30222
< 0.1%
12881
< 0.1%
10351
< 0.1%
5751
< 0.1%
4691
< 0.1%
2031
< 0.1%
1831
< 0.1%
1421
< 0.1%
1411
< 0.1%

total_str_threatened
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct157
Distinct (%)3.4%
Missing8530
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean115.5108907
Minimum0
Maximum175000
Zeros3019
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size103.0 KiB
2022-06-04T10:27:38.978285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile50
Maximum175000
Range175000
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3002.998347
Coefficient of variation (CV)25.99753435
Kurtosis2561.204375
Mean115.5108907
Median Absolute Deviation (MAD)0
Skewness47.04408982
Sum535624
Variance9017999.073
MonotonicityNot monotonic
2022-06-04T10:27:39.302014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03019
 
22.9%
1282
 
2.1%
2174
 
1.3%
3144
 
1.1%
4107
 
0.8%
5100
 
0.8%
656
 
0.4%
756
 
0.4%
1051
 
0.4%
851
 
0.4%
Other values (147)597
 
4.5%
(Missing)8530
64.8%
ValueCountFrequency (%)
03019
22.9%
1282
 
2.1%
2174
 
1.3%
3144
 
1.1%
4107
 
0.8%
5100
 
0.8%
656
 
0.4%
756
 
0.4%
851
 
0.4%
936
 
0.3%
ValueCountFrequency (%)
1750001
< 0.1%
600002
< 0.1%
377912
< 0.1%
199982
< 0.1%
112001
< 0.1%
91461
< 0.1%
70091
< 0.1%
53111
< 0.1%
40502
< 0.1%
30001
< 0.1%

TotalDays
Real number (ℝ)

MISSING
ZEROS

Distinct157
Distinct (%)1.4%
Missing1988
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean10.88066911
Minimum-35
Maximum308
Zeros2439
Zeros (%)18.5%
Negative11
Negative (%)0.1%
Memory size103.0 KiB
2022-06-04T10:27:39.700838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-35
5-th percentile0
Q11
median3
Q310
95-th percentile54
Maximum308
Range343
Interquartile range (IQR)9

Descriptive statistics

Standard deviation22.47739727
Coefficient of variation (CV)2.065810204
Kurtosis45.93079689
Mean10.88066911
Median Absolute Deviation (MAD)3
Skewness5.240305141
Sum121635
Variance505.2333881
MonotonicityNot monotonic
2022-06-04T10:27:40.079143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02439
18.5%
11622
12.3%
21152
8.7%
3815
 
6.2%
4611
 
4.6%
5483
 
3.7%
6401
 
3.0%
7338
 
2.6%
8254
 
1.9%
9236
 
1.8%
Other values (147)2828
21.5%
(Missing)1988
15.1%
ValueCountFrequency (%)
-351
 
< 0.1%
-110
 
0.1%
02439
18.5%
11622
12.3%
21152
8.7%
3815
 
6.2%
4611
 
4.6%
5483
 
3.7%
6401
 
3.0%
7338
 
2.6%
ValueCountFrequency (%)
3082
< 0.1%
3071
 
< 0.1%
3064
< 0.1%
3051
 
< 0.1%
3042
< 0.1%
3011
 
< 0.1%
3001
 
< 0.1%
2911
 
< 0.1%
2791
 
< 0.1%
2721
 
< 0.1%

Interactions

2022-06-04T10:27:21.600542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:50.684057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:54.224218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:57.575568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:01.487181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:05.187145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:08.662986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:11.631133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:14.831246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:18.213539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:21.929853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:51.018817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:54.547445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:57.980951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:02.202989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:05.519201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:08.949860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:11.989948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:15.171978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:18.568948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:27:22.275548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:51.388332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-04T10:26:54.879139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-04T10:27:41.750650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-04T10:27:42.411599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-04T10:27:43.083806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-04T10:27:43.747474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-06-04T10:27:26.793444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-04T10:27:27.711099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-04T10:27:28.691819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

UniqueFireIdentifierIncidentNameFireDiscoveryDateTimeFireCauseDailyAcresEstimatedCostToDateFireMgmtComplexityFireToDatePOOCountyPOOStatePOOLandownerKindIncidentTypeCategoryPredominantFuelGroupFireDiscoveryYearFireDiscoveryMonthkeyNumber InjuriesNumber FatalitiesFinal Fire Acre Quantitytotal_str_damagedtotal_str_threatenedTotalDays
02020-TXTXS-200317Park2020-07-21 22:37:08+00:00Human512.001.0Type 5 Incident2020-07-22 12:00:00+00:00ShackelfordUS-TXPrivateWFNaN202072020-07-21TXSHACKELFORD0.00.0512.000.00.01.0
12016-FLFLS-2016120122Quincy2016-03-09 19:30:00+00:00Human45.000.0Type 5 Incident2016-03-18 12:59:59+00:00BrevardUS-FLNaNWFNaN201632016-03-09FLBREVARD0.00.045.000.00.09.0
22014-LASBR-000015MALLARD FIRE2014-08-18 23:00:00+00:00Natural1062.002000.0NaNNaTCameronUS-LAFederalWFNaN201482014-08-18LACAMERONNaNNaNNaNNaNNaNNaN
32018-TXTXS-000298Arrington Ranch2018-03-23 16:34:59+00:00Human400.001.0Type 5 Incident2018-03-24 12:59:59+00:00CarsonUS-TXNaNWFNaN201832018-03-23TXCARSON0.00.02964.000.00.01.0
42016-CAKRN-032270RANGE2016-08-26 17:29:59+00:00Undetermined516.001450000.0Type 3 Incident2016-08-28 12:59:59+00:00KernUS-CANaNWFNaN201682016-08-26CAKERN0.00.0600.040.00.02.0
52020-NVECFX-010145Cedar2020-07-19 23:00:00+00:00Natural5985.903900000.0Type 4 Incident2020-08-03 01:30:00+00:00ElkoUS-NVPrivateWFBrush202072020-07-19NVELKO0.00.05986.900.00.015.0
62016-NMN4S-004024Corazon Peak2016-07-22 18:59:59+00:00Natural1300.005000.0Type 4 Incident2016-07-24 12:00:00+00:00San MiguelUS-NMNaNWFNaN201672016-07-22NMSAN MIGUEL0.00.01301.500.00.02.0
72017-NVWID-020138Barrett Springs2017-07-09 03:11:00+00:00Natural3295.82500000.0Type 4 Incident2017-07-10 01:30:00+00:00HumboldtUS-NVFederalWFNaN201772017-07-09NVHUMBOLDT0.00.055.380.00.01.0
82016-AKUYD-000303False Alarm 92016-06-25 20:21:58+00:00Natural0.100.0NaNNaTYukon-KoyukukUS-AKFederalWFNaN201662016-06-25AKYUKON-KOYUKUKNaNNaNNaNNaNNaNNaN
92016-ALALF-160511Turkey Day2016-11-24 13:01:49+00:00Human22.0050000.0Type 4 Incident2016-12-06 12:30:00+00:00CleburneUS-ALFederalWFNaN2016112016-11-24ALCLEBURNE0.00.02.001.08.012.0

Last rows

UniqueFireIdentifierIncidentNameFireDiscoveryDateTimeFireCauseDailyAcresEstimatedCostToDateFireMgmtComplexityFireToDatePOOCountyPOOStatePOOLandownerKindIncidentTypeCategoryPredominantFuelGroupFireDiscoveryYearFireDiscoveryMonthkeyNumber InjuriesNumber FatalitiesFinal Fire Acre Quantitytotal_str_damagedtotal_str_threatenedTotalDays
131572022-GAGAS-072203Gillis Springs2022-03-07 18:00:00+00:00HumanNaN1000.0Type 5 Incident2022-03-07 18:00:00+00:00TreutlenUS-GAPrivateWFNaN202232022-03-07GATREUTLENNaNNaNNaNNaNNaN0.0
131582022-MSMSS-224401085Poplarville, 279 Bamacker Drive2022-03-05 04:44:59+00:00UndeterminedNaN30000.0Type 5 Incident2022-03-05 05:00:00+00:00Pearl RiverUS-MSPrivateWFNaN202232022-03-05MSPEARL RIVERNaNNaNNaNNaNNaN0.0
131592022-FLFLS-221400169TRIPLE 2 POLK2022-03-06 22:23:00+00:00UndeterminedNaN0.0Type 5 Incident2022-03-07 19:02:00+00:00PolkUS-FLPrivateWFNaN202232022-03-06FLPOLKNaNNaNNaNNaNNaN1.0
131602022-MSMSS-224301065Tylertown, 504-446 Mississippi 5832022-03-04 23:45:00+00:00HumanNaN32600.0Type 5 Incident2022-03-05 00:00:00+00:00WalthallUS-MSPrivateWFNaN202232022-03-04MSWALTHALLNaNNaNNaNNaNNaN1.0
131612022-FLFLS-000231ROCK BLUFF***NOV*** (39) 02312022-03-03 19:33:00+00:00HumanNaN0.0Type 5 Incident2022-03-07 19:05:00+00:00LibertyUS-FLPrivateWFNaN202232022-03-03FLLIBERTYNaNNaNNaNNaNNaN4.0
131622022-NCNCS-220013River Road #42022-03-07 17:00:00+00:00UndeterminedNaN45000.0Type 4 Incident2022-03-07 17:00:00+00:00CravenUS-NCPrivateWFNaN202232022-03-07NCCRAVENNaNNaNNaNNaNNaN0.0
131632022-GAGAS-082201Chatham Osteen2022-03-07 20:15:00+00:00HumanNaN11900.0Type 4 Incident2022-03-07 20:15:00+00:00ChathamUS-GAPrivateWFNaN202232022-03-07GACHATHAMNaNNaNNaNNaNNaN0.0
131642022-KSWBX-000159PawPaw Creek2022-03-05 20:29:59+00:00Human1300.016000.0Type 4 Incident2022-03-05 20:29:59+00:00WabaunseeUS-KSPrivateWFNaN202232022-03-05KSWABAUNSEENaNNaNNaNNaNNaN0.0
131652022-FLFLS-22020022CHIPOLA COMPLEX2022-03-04 16:44:59+00:00NaN15253.0423348.0Type 2 Incident2022-03-07 11:45:00+00:00GulfUS-FLPrivateCXNaN202232022-03-04FLGULFNaNNaNNaNNaNNaN3.0
131662022-MSMSS-224200987Moses Ln and Hwy 842022-03-04 18:30:00+00:00Human108.021600.0Type 5 Incident2022-03-04 18:45:00+00:00Jefferson DavisUS-MSPrivateWFNaN202232022-03-04MSJEFFERSON DAVISNaNNaNNaNNaNNaN0.0